The generalized slenderness‐based resistance method for the design of SHS and RHS

Toffolon, Andrea; Meng, Xin; Taras, Andreas; Gardner, Leroy

doi:10.1002/stco.201900036

Cited by 18 publications

(30 citation statements)

References 17 publications

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“…Diese Annahme richtet sich nach dem Vorgehen in [19] für die Entwicklung europäischer Knickkurven. Bestätigt wurden diese Ansätze ebenfalls durch die Arbeit in [11,12] sowie zahlreiche weitere, z. T. eigene Untersuchungen in [20,21].…”

Section: Ansatz Von Imperfektionenunclassified

Kippstabilität von Trägern mit Drehbettung – Einfluss und Anwendung der neuen Regelungen in prEN1993‐1‐1:2020

Müller

Taras

2021

Stahlbau

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Lateral torsional buckling of girders with rotational restraintsinfluence and application of the new rules in prEN1993-1-1:2020 This paper presents the results of a study that investigated the effects of the innovations introduced in the draft standard for the 2nd generation of EN 1993-1-1 for the lateral-torsional buckling (LTB) verification of beams with I and H cross-sections on the regulations for the calculation of the minimum stiffness of a rotational restraint to avoid LTB. In the first part of the paper, the background of the existing and new regulations is discussed together with previous proposals for adapting the definition of the minimum rotation bedding. Then, based on theoretical considerations and numerical calculations of the realistic loadbearing capacity of rotationally restrained beams, it is shown that the new regulations for LTB in prEN1993-1-1 lead to very precise predictions of the load-bearing capacity of rotationally restrained girders and beams, provided that rotational stiffness is also taken into account in the determination of the corresponding slenderness for LTB. In addition, it is shown that the currently valid specifications for the minimum rotational stiffness contained in the National Annex of DIN EN 1993-1-1 are also sufficiently accurate for practical use and do not contradict the newly introduced regulations for torsional buckling. The planned tightening of the regulations on the minimum rotational stiffness in prEN 1993-1-1, Annex D are thus shown to be unnecessary.

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Section: Ansatz Von Imperfektionenunclassified

Kippstabilität von Trägern mit Drehbettung – Einfluss und Anwendung der neuen Regelungen in prEN1993‐1‐1:2020

Müller

Taras

2021

Stahlbau

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“…The results are thereby categorised according to the Eurocode 3 classification method [8], and the highest scatter is shown in class 1 and 2, where the design rules are simplified and more conservative. The gains in strength and accuracy remarkable for class 3 and 4, especially compared to the current design codes (see [2] for more details).…”

Section: Comparison Of the Gsrm Design Values And DL Predictionsmentioning

confidence: 89%

“…In this section, the results of the numerical parametric study are compared to the GSRM design rules for the cross-sectional capacity of SHS and RHS, as well as the prediction of the DNN model. The indices "GMNIA", "GSRM" and "DNN" refer to GMNIA numerical results, the GSRM design rules in [2] and the predictions of the newly developed DNN model. Figure 14 gives an overview of accuracy and safety of the new GSRM design rules by normalizing the numerical test results by the rule in the GRSM.…”

Section: Comparison Of the Gsrm Design Values And DL Predictionsmentioning

confidence: 99%

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Deep Learning based method for the prediction of the buckling resistance of SHS and RHS

2021

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This paper proposes a data‐driven and deep learning based model for the prediction of the buckling load of square and rectangular hollow sections (SHS and RHS) based on Finite Element Method (FEM) results of the local buckling effects. The data was elaborated during the EU‐funded research project – HOLLOSSTAB (2016‐2019), where a new design method – termed the “Generalized Slenderness‐based Resistance Method” (GSRM) ‐ was developed, aiming to simplify through a unified analytical approach time consuming computational analyses for the prediction of the local resistance of SHS and RHS. This method further generalizes the concepts of established design methods such as the Direct Strength Method. This paper investigates an alternative approach to the development of design rules for this same mechanical problem: the application of deep learning models for the prediction of the buckling load through simple input parameters, such as geometric quantities, material values and applied load to circumvent the application of the analytical formulae. Thereby, a Deep Neural Network (DNN) model is created and calibrated for the prediction of the buckling loads of SHS and RHS based on the training data given by the extensive FEM parametric study. A statistical evaluation in accordance with EN1990 elaborates a partial safety factor for the proposed DNN. The predictions from the DNN are then compared to the numerical results from FEM analyses and to the predictions of the GSRM and EC3. Since the numerical results were originally validated against large scale experimental tests, this newly developed DNN makes use of a very large set of very representative (numerical) tests. A further comparison assesses the computed partial safety factor and the related quantities of interest for the DNN to the respective counterparts of the GSRM method. This work represents one of the first steps for the development of design methods in the field structural design adopting the most recent advances in computer sciences, and future enhancements of the currently developed models are discussed and proposed as a conclusion.

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“…The GSRM for the cross-section design of CHS and EHS, including a strength-based approach and a deformation-based approach, is then proposed. The reader may refer to a second paper by the authors in the same issue of this journal [3], which addresses the crosssection design of square and rectangular hollow sections. Significant improvements over EC3, in terms of accuracy and simplicity, are clearly revealed through comparisons with the test and numerical data, and the subsequent reliability analyses demonstrate the suitability of the GSRM for application in future structural design.…”

Section: Introductionmentioning

confidence: 99%

The generalised slenderness‐based resistance method for the design of CHS and EHS

et al. 2019

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Selected, extended paper from the SDSS 2019 special session ECCS/TC8 – Structural Stability This paper presents the development and assessment of an innovative cross‐section design method for structural steel circular and elliptical hollow sections (CHS and EHS) – the generalised slenderness‐based resistance method (GSRM). A numerical simulation programme was first conducted to expand the data pool for CHS and EHS. Finite element (FE) models were established, validated against existing test data and then utilised for parametric studies, where a total of over 3 700 cross‐section resistance data were numerically generated. The development of the GSRM for CHS and EHS is then presented. Key design parameters, including the reference resistances and the generalised local slenderness, are initially defined. The general design procedure is subsequently introduced. Two design alternatives for CHS and EHS – a strength‐based approach, and a deformation‐based approach based on the continuous strength method (CSM), are developed and presented. Finally, the proposed GSRM is assessed using the previously collected test results and freshly generated FE data, where excellent accuracy and consistency in the resistance predictions are clearly revealed for all loading scenarios. Subsequent reliability analyses demonstrate that the current partial safety factor used in EN 1993‐1‐1 can be applied to the GSRM, achieving an appropriate level of reliability.

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The generalized slenderness‐based resistance method for the design of SHS and RHS

Cited by 18 publications

References 17 publications

Kippstabilität von Trägern mit Drehbettung – Einfluss und Anwendung der neuen Regelungen in prEN1993‐1‐1:2020

Kippstabilität von Trägern mit Drehbettung – Einfluss und Anwendung der neuen Regelungen in prEN1993‐1‐1:2020

Deep Learning based method for the prediction of the buckling resistance of SHS and RHS

The generalised slenderness‐based resistance method for the design of CHS and EHS

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